An Unscented Particle Filter Approach to Estimating Real-Time Traffic State
Estimating the real-time traffic state is important to fulfill the intelligent traffic management, it is therefore of great interest to obtain accurate estimation of the realtime traffic so that adaptive control mechanisms can be carried out accordingly. The macroscopic traffic flow is adopted as the model of freeway, it is considered as connected by same distance segments; the traffic sensors are placed at the conjunction of these segments, their numbers are much less than the traffic state to be estimated. The compression state space is adopted, the model parameters is taken as traffic state to be estimated not constant value. An unscented particle filter (UPF) method is proposed to improve the estimation accuracy of real-time traffic state. The simulation results indicate that the unscented particle filter can increase the accuracy of the estimation in terms of the root mean square error (RMSE), compared with the Extended Kalman filter (EKF).
Extended Kalman filter (EKF) Unscented Particle filter (UPF) macroscopic traffic flow model
Zheng Yongjun Li Wenjun Sun Bin Jin Yanhua
College of Metrology and Measurement Engineering China Jiliang University Hangzhou, Peoples Republi College of Foreign Languages Zhejiang Sci-Tech University Hangzhou,Peoples Republic of China
国际会议
张家界
英文
471-474
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）